Submission¶

Put the ipynb file and html file in the github branch you created in the last assignment and submit the link to the commit in brightspace

In [1]:
from plotly.offline import init_notebook_mode
import plotly.io as pio
import plotly.express as px

init_notebook_mode(connected=True)
pio.renderers.default = "plotly_mimetype+notebook"
In [2]:
#load data
df = px.data.gapminder()
df.head()
Out[2]:
country continent year lifeExp pop gdpPercap iso_alpha iso_num
0 Afghanistan Asia 1952 28.801 8425333 779.445314 AFG 4
1 Afghanistan Asia 1957 30.332 9240934 820.853030 AFG 4
2 Afghanistan Asia 1962 31.997 10267083 853.100710 AFG 4
3 Afghanistan Asia 1967 34.020 11537966 836.197138 AFG 4
4 Afghanistan Asia 1972 36.088 13079460 739.981106 AFG 4

Question 1:¶

Recreate the barplot below that shows the population of different continents for the year 2007.

Hints:

  • Extract the 2007 year data from the dataframe. You have to process the data accordingly
  • use plotly bar
  • Add different colors for different continents
  • Sort the order of the continent for the visualisation. Use axis layout setting
  • Add text to each bar that represents the population
In [9]:
df_2007 = df[df['year']==2007]

# Group the filtered data by continent and calculate the sum of numeric columns
df_2007_continent_pop = df_2007.groupby('continent').sum(numeric_only=False)

# Create a bar chart using Plotly Express
fig = px.bar(df_2007_continent_pop, 
             x="pop", 
             y=df_2007_continent_pop.index, 
             orientation='h', 
             color=df_2007_continent_pop.index)

# Customize the layout of the chart: hide the legend
fig.update_layout(showlegend=False)

fig.show()
In [5]:
# YOUR CODE HERE

Question 2:¶

Sort the order of the continent for the visualisation

Hint: Use axis layout setting

In [10]:
fig.update_layout(yaxis={"categoryorder": "total ascending"})
fig.show()
In [6]:
# YOUR CODE HERE

Question 3:¶

Add text to each bar that represents the population

In [8]:
fig.update_traces(textposition="outside", texttemplate='%{text:.2s}')
fig.show()
In [7]:
# YOUR CODE HERE

Question 4:¶

Thus far we looked at data from one year (2007). Lets create an animation to see the population growth of the continents through the years

In [11]:
df_continent_year = df.groupby(["continent", "year"], as_index=False).sum(numeric_only=False)

fig = px.bar(df_continent_year, x="pop", y="continent", color="continent",
        animation_frame="year", animation_group="continent", range_x=[0,4000000000], orientation="h")

fig.update_layout(showlegend=False, yaxis={"categoryorder": "total ascending"})
fig.show()
In [9]:
# YOUR CODE HERE

Question 5:¶

Instead of the continents, lets look at individual countries. Create an animation that shows the population growth of the countries through the years

In [13]:
df_country_year = df.groupby(["country", "year"], as_index=False).sum(numeric_only=False)

fig = px.bar(df_country_year, x="pop", y="country", color="country",
        animation_frame="year", animation_group="country", range_x=[0,1500000000], orientation="h")

fig.update_layout(showlegend=False, yaxis={"categoryorder": "total ascending"})
fig.show()
In [11]:
# YOUR CODE HERE

Question 6:¶

Clean up the country animation. Set the height size of the figure to 1000 to have a better view of the animation

In [14]:
fig.update_layout(height=1000)
fig.show()
In [12]:
# YOUR CODE HERE

Question 7:¶

Show only the top 10 countries in the animation

Hint: Use the axis limit to set this.

In [15]:
amount_of_countries = df["country"].nunique()

fig.update_layout(height=450)
fig.update_yaxes(range=[amount_of_countries-10.5, amount_of_countries-0.5]) 
fig.show()
In [13]:
# YOUR CODE HERE